Experimental results. We compare our approach with current state-of-the-art methods [1]} [2]} [3]} [4]} [5]} [6]} [7]}. We adopt the same strong/weak split, \(i.e.,\)  1.4K strongly annotated images and 9K weakly annotated images. Table REF  summarizes the comparison. To the best of our knowledge, most semi-supervised methods report the results on Deeplab v1-vgg16, only [6]} and [7]} are performed on resnet50 [6]} and Deeplab v2-resnet101 [7]}. Therefore, for a fair comparison, we illustrate the backbone used for each approach in Table REF , and evaluate our method on different backbones. From the results, we can see that our proposed method outperforms other methods and achieves the best mIoU of 67.5% and 67.9% (v1-vgg16) and 77.1% and 77.2% (v2-resnet101) on val and test sets, respectively. It should be noted that the performance on val set outperforms the fully-supervised model (76.5%) by 0.6% using v2-resnet101 segmentation network. We explain this surprising result and find a possible explanation. We believe the pseudo labels corrected by GAT can successfully highlight some instances which are improperly labeled in ground truth annotations, thus resulting in an improvement in the performance of the segmentation network, which exceeds the fully-supervised model. Moreover, our proposed method dedicates to generate more accurate pseudo-labels, and thus the performance can be further improved by incorporating the strong-weak dual-branch network [7]}.
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